#
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import random
from enum import Enum
from typing import Union
import numpy as np
try:
from dm_control import suite
from dm_control.suite.wrappers import pixels
except ImportError:
from rl_coach.logger import failed_imports
failed_imports.append("DeepMind Control Suite")
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
from rl_coach.spaces import BoxActionSpace, ImageObservationSpace, VectorObservationSpace, StateSpace
class ObservationType(Enum):
Measurements = 1
Image = 2
Image_and_Measurements = 3
# Parameters
class ControlSuiteEnvironmentParameters(EnvironmentParameters):
def __init__(self, level=None):
super().__init__(level=level)
self.observation_type = ObservationType.Measurements
self.default_input_filter = ControlSuiteInputFilter
self.default_output_filter = ControlSuiteOutputFilter
@property
def path(self):
return 'rl_coach.environments.control_suite_environment:ControlSuiteEnvironment'
"""
ControlSuite Environment Components
"""
ControlSuiteInputFilter = NoInputFilter()
ControlSuiteOutputFilter = NoOutputFilter()
control_suite_envs = {':'.join(env): ':'.join(env) for env in suite.BENCHMARKING}
# Environment
[docs]class ControlSuiteEnvironment(Environment):
def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters,
target_success_rate: float=1.0, seed: Union[None, int]=None, human_control: bool=False,
observation_type: ObservationType=ObservationType.Measurements,
custom_reward_threshold: Union[int, float]=None, **kwargs):
"""
:param level: (str)
A string representing the control suite level to run. This can also be a LevelSelection object.
For example, cartpole:swingup.
:param frame_skip: (int)
The number of frames to skip between any two actions given by the agent. The action will be repeated
for all the skipped frames.
:param visualization_parameters: (VisualizationParameters)
The parameters used for visualizing the environment, such as the render flag, storing videos etc.
:param target_success_rate: (float)
Stop experiment if given target success rate was achieved.
:param seed: (int)
A seed to use for the random number generator when running the environment.
:param human_control: (bool)
A flag that allows controlling the environment using the keyboard keys.
:param observation_type: (ObservationType)
An enum which defines which observation to use. The current options are to use:
* Measurements only - a vector of joint torques and similar measurements
* Image only - an image of the environment as seen by a camera attached to the simulator
* Measurements & Image - both type of observations will be returned in the state using the keys
'measurements' and 'pixels' respectively.
:param custom_reward_threshold: (float)
Allows defining a custom reward that will be used to decide when the agent succeeded in passing the environment.
"""
super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate)
self.observation_type = observation_type
# load and initialize environment
domain_name, task_name = self.env_id.split(":")
self.env = suite.load(domain_name=domain_name, task_name=task_name, task_kwargs={'random': seed})
if observation_type != ObservationType.Measurements:
self.env = pixels.Wrapper(self.env, pixels_only=observation_type == ObservationType.Image)
# seed
if self.seed is not None:
np.random.seed(self.seed)
random.seed(self.seed)
self.state_space = StateSpace({})
# image observations
if observation_type != ObservationType.Measurements:
self.state_space['pixels'] = ImageObservationSpace(shape=self.env.observation_spec()['pixels'].shape,
high=255)
# measurements observations
if observation_type != ObservationType.Image:
measurements_space_size = 0
measurements_names = []
for observation_space_name, observation_space in self.env.observation_spec().items():
if len(observation_space.shape) == 0:
measurements_space_size += 1
measurements_names.append(observation_space_name)
elif len(observation_space.shape) == 1:
measurements_space_size += observation_space.shape[0]
measurements_names.extend(["{}_{}".format(observation_space_name, i) for i in
range(observation_space.shape[0])])
self.state_space['measurements'] = VectorObservationSpace(shape=measurements_space_size,
measurements_names=measurements_names)
# actions
self.action_space = BoxActionSpace(
shape=self.env.action_spec().shape[0],
low=self.env.action_spec().minimum,
high=self.env.action_spec().maximum
)
# initialize the state by getting a new state from the environment
self.reset_internal_state(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
scale = 1
if self.human_control:
scale = 2
if not self.native_rendering:
self.renderer.create_screen(image.shape[1]*scale, image.shape[0]*scale)
self.target_success_rate = target_success_rate
def _update_state(self):
self.state = {}
if self.observation_type != ObservationType.Measurements:
self.pixels = self.last_result.observation['pixels']
self.state['pixels'] = self.pixels
if self.observation_type != ObservationType.Image:
self.measurements = np.array([])
for sub_observation in self.last_result.observation.values():
if isinstance(sub_observation, np.ndarray) and len(sub_observation.shape) == 1:
self.measurements = np.concatenate((self.measurements, sub_observation))
else:
self.measurements = np.concatenate((self.measurements, np.array([sub_observation])))
self.state['measurements'] = self.measurements
self.reward = self.last_result.reward if self.last_result.reward is not None else 0
self.done = self.last_result.last()
def _take_action(self, action):
if type(self.action_space) == BoxActionSpace:
action = self.action_space.clip_action_to_space(action)
self.last_result = self.env.step(action)
def _restart_environment_episode(self, force_environment_reset=False):
self.last_result = self.env.reset()
def _render(self):
pass
def get_rendered_image(self):
return self.env.physics.render(camera_id=0)
def get_target_success_rate(self) -> float:
return self.target_success_rate